Amit Zac

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Zac

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Amit

ORCID

0000-0001-6858-7547

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Publications 1 - 8 of 8

Dark patterns and consumer vulnerability
Zac, Amit; Huang, Yu-Chun; von Moltke, Amédée; et al. (2025)
Behavioural Public Policy
Dark patterns that manipulate consumer behaviour are now a pervasive feature of digital markets. Depending on the choice architecture utilised, they can affect the perception, behaviour and purchasing patterns of online consumers. Using a novel empirical design, we find strong evidence that individuals across all groups are susceptible to dark patterns, and only weak evidence that user susceptibility is materially affected by commonly used general proxies for consumer vulnerability (such as income, educational attainment or age). Our conclusions provide empirical support for broad restrictions on the use of dark patterns, such as those contained in the EU's Digital Services Act, that protect all consumer groups. Our study also finds that added friction, in the form of required payment action following successful deployment of dark patterns, reduces their effectiveness. This insight highlights the instances in which dark patterns would be most effective - when no further action is required by the user. Consumer vulnerability is therefore more pronounced when dealing with online providers who store users' payment details and can rely on a 'single click' to complete the purchase.
Competition Law Enforcement and Household Inequality in the United Kingdom
Decker, Christopher; Zac, Amit; Casti, Carola; et al. (2022)
Journal of Competition Law & Economics
Using a comprehensive database of all the decisions made under European and U.K. competition laws over the 15-year period to 2020, alongside households’ consumption and market data, we estimate the level and distribution of the savings from enforcement across the United Kingdom. We find that competition law enforcement generated greater proportional savings for lower- and average-income households relative to the wealthiest households. Our estimations indicate average savings of 2.5 percent of the annual household budget for the lowest-income households, 2.1 percent for the average household, and 1.8 percent for the highest-income household. While proportionally greater savings for lower- and average-income households from competition law are observed in most years, in some years, higher-income households saved more. Our results bring to light the variables that affect the distribution of savings. Among them are the enforcement tool applied, the sectors in which enforcement action took place, and the enforcement body. We further illustrate how the public enforcement of competition law affects economic disparity and could potentially be used in a more structured, transparent, and systematic way to address societal concerns about increasing inequality.
SUSTAINABLESIGNALS: An AI Approach for Inferring Consumer Product Sustainability
Lin, Tong; Xu, Tianliang; Zac, Amit; et al. (2023)
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23)
The everyday consumption of household goods is a significant source of environmental pollution. The increase of online shopping affords an opportunity to provide consumers with actionable feedback on the social and environmental impact of potential purchases, at the exact moment when it is relevant. Unfortunately, consumers are inundated with ambiguous sustainability information. For example, greenwashing can make it difficult to identify environmentally friendly products. The highest-quality options, such as Life Cycle Assessment (LCA) scores or tailored impact certificates (e.g., environmentally friendly tags), designed for assessing the environmental impact of consumption, are ineffective in the setting of online shopping. They are simply too costly to provide a feasible solution when scaled up, and often rely on data from self-interested market players. We contribute an analysis of this online environment, exploring how the dynamic between sellers and consumers surfaces claims and concerns regarding sustainable consumption. In order to better provide information to consumers, we propose a machine learning method that can discover signals of sustainability from these interactions. Our method, SUSTAINABLESIGNALS, is a first step in scaling up the provision of sustainability cues to online consumers.
The Effects of Competition Law on Inequality — An Incidental By-Product or a Path for Societal Change?
Ezrachi, Ariel; Zac, Amit; Decker, Christopher (2023)
Journal of Antitrust Enforcement
Rising economic inequality presents society with unprecedented challenges. Direct instruments designed to address these worrying trends have often under performed. As a result, we find ourselves on a potentially dangerous and downward path. In this article we explore whether, in parallel to other efforts to mitigate the rise of inequality, there can be a role for competition law in the quest to reduce the widening inequality gap. We begin by outlining the possible relationship between competition law enforcement, market power, and economic inequality. We supplement the theoretical discussion with a review of empirical analysis of these linkages. We look at macro and micro data and emphasize the role of labour compensation as a key mechanism which links competition law enforcement, competition dynamics, and economic inequality. We then reflect on the policy implications and possible means to utilize competition enforcement in a manner that could reduce economic inequality.
The Court Speaks, But Who Listens? Automated Compliance Review of the GDPR
Zac, Amit; Wey, Pablo; Bechtold, Stefan; et al. (2024)
Center for Law & Economics Working Paper Series
With the implementation of the General Data Protection Regulation in 2018, the European Union put itself at the forefront of protecting privacy law world-wide. Under the GDPR, data protection agencies may impose fines up to 4% of a firm’s annual worldwide turnover. The largest fines actually imposed now surpass one billion Euro. Still, anecdotal and empirical evidence suggests that many firms violate the GDPR on a regular basis. This could be because such violations may be difficult to detect, or because it may be unclear whether a particular behavior violates the GDPR. This paper analyzes the impact of a drastic example of GDPR enforcement. In July 2020, the European Court of Justice invalidated the EU-US Privacy Shield with immediate effect (“Schrems II”). As a result, many personal data transfers from the European Union to the United States became illegal overnight. We present a unique dataset allowing us not only to observe what firms say about their behavior in privacy policies, but also how firms actually behave. Using machine-learning tools, we analyze the privacy policies of over 7,500 apps on the Spanish Google Play Store and find limited compliance with the Schrems II decision. We validate the quality of our classifier through manual inspection of privacy policies. Using tools from IT security research, we are able to observe the actual personal data traffic flows leaving apps towards the United States after Schrems II. Combining our observations on privacy policies and data traffic flows, our findings on compliance with Schrems II are sobering. A few weeks after Schrems II was decided, only 23% of the studied apps in our sample seem to comply with the decision while 77% seem to violate the GDPR. Over two years after Schrems II, the rate of compliant apps increases, yet we estimate that roughly 45% of the apps are non-compliant. We examine the implications our findings have for the design and enforcement of the GDPR, and discuss how the combination of an automated analysis of contracts and of actual data traffic flows can improve our understanding of how to regulate the digital economy at large scale.
Automated Large-Scale Analysis of Cookie Notice Compliance
Bouhoula, Ahmed; Kubicek, Karel; Zac, Amit; et al. (2024)
Proceedings of the 33rd USENIX Security Symposium
Privacy regulations such as the General Data Protection Regulation (GDPR) require websites to inform EU-based users about non-essential data collection and to request their consent to this practice. Previous studies have documented widespread violations of these regulations. However, these studies provide a limited view of the general compliance picture: they are either restricted to a subset of notice types, detect only simple violations using prescribed patterns, or analyze notices manually. Thus, they are restricted both in their scope and in their ability to analyze violations at scale. We present the first general, automated, large-scale analysis of cookie notice compliance. Our method interacts with cookie notices, e.g., by navigating through their settings. It observes declared processing purposes and available consent options using Natural Language Processing and compares them to the actual use of cookies. By virtue of the generality and scale of our analysis, we correct for the selection bias present in previous studies focusing on specific Consent Management Platforms (CMP). We also provide a more general view of the overall compliance picture using a set of 97k websites popular in the EU. We report, in particular, that 65.4% of websites offering a cookie rejection option likely collect user data despite explicit negative consent.
Competition policy and the labor share
Zac, Amit; Casti, Carola; Decker, Christopher; et al. (2024)
The Journal of Law, Economics & Organization
Recent years have seen intense debate about the causes of the observed decline in the labor share. We extend this inquiry by investigating whether the design and enforcement of competition law and policy are associated with changes in the labor share. Using a panel of 22 industries in 12 OECD economies, we find a positive statistical association between the effectiveness of competition policy and changes in the labor share over the period 1995-2005. This suggests a potential link between the design and effectiveness of competition policy and the labor share, and more broadly to distributional outcomes. Our results reinforce the importance of accounting for country-specific factors, including the design and enforcement of local laws, when examining dynamics in the labor share. The analysis implies that effective competition law and policy could mitigate the decline of the labor share, particularly in settings characterized by low levels of labor protection and limited labor bargaining power. (JEL: E21, E24, E64, J01, J21, K21, L40).
Competition Law and Economic Inequality: A Comparative Analysis of the US Model of Law
Zac, Amit (2022)
Journal of International Economic Law
To what extent does the choice of competition law model correlate with economic inequality? While competition laws have been suggested as potentially contributing to current inequality trends in developed countries and as a viable instrument to address them, there is little empirical evidence on their distributional effects. This article helps fill this gap. It utilizes a comparative legal approach and a unique estimation framework based on the textual similarity to estimate the differences between the US and EU models and provides evidence that countries that adopt a US-style antitrust model are more likely to exhibit higher income inequality levels over time. While this link should not be interpreted causally, it suggests that potential institutional factors might affect the rise of inequality.

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Amit Zac

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